Authors
Abstract
In many of the digital image processing applications, observed image is
modeled to be corrupted by different types of noise that result in a noisy version.
Hence image denoising is an important problem that aims to find an estimate
version from noisy image that is as close to the original image as possible. In this
paper, introduces firstly was applied method of computing one and twodimensional
framelet transform .The applying method reduces heavily processing
time for decomposition of image keeping or overcoming the quality of
reconstructed images. In addition, it cuts heavily the memory demands .Also, the
inverse procedures of all the above transform for multi- dimensional cases
verified. Secondly, many techniques are proposed for denoising of gray scale and
color image. A new threshold method is proposed and compared with the other
thresholding methods. For hard thresholding, PSNR gives (13.548) value while
the PSNR was increased in the proposed soft thresholding, it gives (14.1734)
PSNR value when the noise variance is (20). Some of the above denoising
schemes are tested on Peppers image to find its effect on denoising application.
The noisy version with SNR is equal to (11.9373 dB), the denoising image using
WT with SNR is equal to (17.4661 dB), the denoising image using SWT with
SNR is equal to (18.1459 dB), the denoising image using WPT with SNR is equal
to (19.3640 dB), the denoising image using FT with SNR is equal to (21.9138
dB). Finally the denoising image for color image using FT with SNR is equal to
(27.3443 dB).
Keywords
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